Data-driven exploration and modelling of the interconnectedness of Sustainable Development Goals
Abonyi Lab
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#SDGAction33554
Description
By analysing the time series data collected at national level on the indicators of the Sustainable Development Goals (SDGs), the adaptation pathways of the countries can be identified, and it is possible to group the countries based on SDG problems and successes, which helps the common knowledge accumulation and effective implementation.
The project is carried out by two research groups (MTA-PE “Lendület” Complex Systems Monitoring Research Group and Sustainability Solutions Research Lab) of the Faculty of Engineering of the University of Pannonia with the support of the Hungarian Ministry of Innovation and Technology. The new developed tools are freely published in the spirit of open science, so the analyses can be repeated at any time and the results can be used in the implementation of SDGs worldwide.
The tools developed for comprehensive analysis of the UN Global SDG database and World Bank data are available for everyone and the new scientific findings are continuously published in international scientific journals, giving researchers and stakeholders direct access to new scientific findings and their use in strategy-making. One of the main impacts of this good practice is to provide a benchmarking basis for countries around the world. As the analyses were carried out at national level, it is possible to compare adaptation pathways, that allow countries in a similar situation to share their experiences and good practices in a targeted way.
The research would not have been possible without the support of the Hungarian Ministry of Innovation and Technology. The gestor of methodological developments is the Faculty of Engineering, University of Pannonia.
The methodologies and algorithms developed in this good practice are freely available on the authors' website (https://www.abonyilab.com/), so in accordance with the principles of open science, the results can be reproduced at any time and used for further analyses or to support decision-making in SDG strategy planning.
Published papers: Sebestyén, V., Bulla, M., Rédey, Á., & Abonyi, J. (2019). Network model-based analysis of the goals, targets and indicators of sustainable development for strategic environmental assessment. Journal of environmental management, 238, 126-135. Dörgő, G., Sebestyén, V., & Abonyi, J. (2018). Evaluating the interconnectedness of the sustainable development goals based on the causality analysis of sustainability indicators. Sustainability, 10(10), 3766. Sebestyén, V., Domokos, E., & Abonyi, J. (2020). Focal points for sustainable development strategies—Text mining-based comparative analysis of voluntary national reviews. Journal of environmental management, 263, 110414. Sebestyén, V., Bulla, M., Rédey, Á., & Abonyi, J. (2019). Data-driven multilayer complex networks of sustainable development goals. Data in brief, 25, 104049. Sebestyén, V., Domokos, E., & Abonyi, J. (2020). Multilayer network based comparative document analysis (MUNCoDA). MethodsX, 7, 100902.
The analyses are based on country-specific SDG indicators and thus implicitly include the impacts of COVID-19. Based on time series data, it helps to understand the impacts of COVID-19 on SDGs, so this practice can contribute to better building back through the exploration of causal relationships.
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The analysis, developed in this good practice, covers all geographical units of the world, thus providing valuable information to decision-makers in all countries on the status and progress of SDGs. The results are also valuable for international scientific organizations and researchers with a better understanding of sustainability.
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Viktor, Data-driven exploration and modelling of the interconnectedness of Sustainable Development Goals
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